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Flow Cytometry

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Related Experiment Video

Updated: Mar 6, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

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Cluster Flow: A user-friendly bioinformatics workflow tool.

Philip Ewels¹, Felix Krueger², Max Käller³

¹Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm, Sweden.

|May 20, 2017

Summary

This summary is machine-generated.

Cluster Flow simplifies bioinformatics analysis by providing an easy-to-install pipeline tool. It offers pre-built modules for next-generation sequencing (NGS) data processing, reducing setup time and enhancing reproducibility.

Keywords:

Bioinformatics Data analysis Next-generation sequencing Parallel computing Pipeline Workflow

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Last Updated: Mar 6, 2026

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Published on: February 8, 2017

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Pipeline tools are crucial for managing complex bioinformatics workflows.
Existing tools often have steep learning curves and require extensive setup.
Reproducibility and efficiency are key challenges in bioinformatics analysis.

Purpose of the Study:

To introduce Cluster Flow, a user-friendly and flexible bioinformatics pipeline management tool.
To simplify the installation and execution of next-generation sequencing (NGS) analysis pipelines.
To provide a readily usable solution with pre-built modules for common bioinformatics tasks.

Main Methods:

Development of Cluster Flow, a bioinformatics pipeline tool with a simple syntax.
Inclusion of 40 pre-built modules for common NGS processing steps.
Implementation of core helper functions to automate NGS procedures.

Main Results:

Cluster Flow offers quick and easy installation, minimizing setup overhead.
The tool comes with 40 ready-to-use modules for immediate application.
Pipelines can be easily assembled and modified using a straightforward syntax.

Conclusions:

Cluster Flow significantly reduces the workload and complexity associated with bioinformatics pipeline management.
The tool enhances the reproducibility of analysis results through simplified workflow execution.
Cluster Flow provides an accessible and efficient solution for common NGS data processing needs.